title: Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis.
abstract: In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce.
authors.affiliation: IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, València, Spain. vesaltor@upv.es
authors.forename: Salvador
authors.initials: S
authors.lastname: Tortajada
authors.forename: Elies
authors.initials: E
authors.lastname: Fuster-Garcia
authors.forename: Javier
authors.initials: J
authors.lastname: Vicente
authors.forename: Pieter
authors.initials: P
authors.lastname: Wesseling
authors.forename: Franklyn A
authors.initials: FA
authors.lastname: Howe
authors.forename: Margarida
authors.initials: M
authors.lastname: Julià-Sapé
authors.forename: Ana-Paula
authors.initials: AP
authors.lastname: Candiota
authors.forename: Daniel
authors.initials: D
authors.lastname: Monleón
authors.forename: Angel
authors.initials: A
authors.lastname: Moreno-Torres
authors.forename: Jesús
authors.initials: J
authors.lastname: Pujol
authors.forename: John R
authors.initials: JR
authors.lastname: Griffiths
authors.forename: Alan
authors.initials: A
authors.lastname: Wright
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Peet
authors.forename: M Carmen
authors.initials: MC
authors.lastname: Martínez-Bisbal
authors.forename: Bernardo
authors.initials: B
authors.lastname: Celda
authors.forename: Carles
authors.initials: C
authors.lastname: Arús
authors.forename: Montserrat
authors.initials: M
authors.lastname: Robles
authors.forename: Juan Miguel
authors.initials: JM
authors.lastname: García-Gómez
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000175:diagnosis*; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D016002:Discriminant Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging